Machine learning engineers play a crucial role in developing, optimizing, and maintaining algorithms that allow computers to learn and make predictions based on data. In essence, their job is to design systems capable of recognizing patterns and making decisions with minimal human intervention. These engineers are primarily focused on ensuring that machine learning models are robust, scalable, and efficient for real-world applications. Their job doesn’t just involve building algorithms; they are also responsible for deploying those models into production environments and ensuring they continue to function correctly over time.
Machine learning engineers work closely with data scientists, but their responsibilities are distinct in terms of the development lifecycle. While data scientists focus on extracting actionable insights from data and exploring new data features, machine learning engineers take these insights and create production-ready systems that can handle vast amounts of data and perform real-time predictions. These systems might use deep learning techniques, decision trees, or support vector machines, depending on the nature of the problem at hand.
In addition to coding, machine learning engineers are responsible for the scalability of the algorithms they create. They must ensure that these models can process large datasets efficiently, particularly when dealing with high-dimensional data or streaming data. Given that machine learning models often require high-performance computing resources, engineers must also understand how to optimize their models for better memory management, performance, throughput, and latency. Furthermore, they are often tasked with debugging issues related to model performance, and ensuring that their models stay up to date as new data becomes available.
When deploying machine learning models, these engineers must ensure that the models integrate smoothly with other software systems within an organization. This often requires collaboration with software developers, infrastructure teams, and data scientists. Machine learning engineers need to have a strong understanding of software engineering principles to ensure that the models they build can be maintained and scaled over time. These professionals may also be responsible for version control of the experiments, metadata, and models that they develop, ensuring that the right model version is being used and that the performance of the model is continuously monitored.
Required Skills and Knowledge for Machine Learning Engineers
Machine learning engineers are expected to possess a robust skill set, especially in the areas of programming, software engineering, and data modeling. A strong foundation in programming languages like Python, Scala, or C++ is essential, as well as proficiency in using machine learning frameworks like TensorFlow, PyTorch, and MXNet. They must also be familiar with deployment tools like Docker and Kubernetes to facilitate the containerization and orchestration of their models in production environments.
In addition to coding, machine learning engineers need a solid understanding of data structures and algorithms. These concepts are crucial when optimizing models to run efficiently on different hardware platforms, such as GPUs or edge devices. Engineers must also understand the computational requirements of the models they develop and be able to tailor the models accordingly. This might involve adjusting algorithms to use less memory or to run faster on specific types of hardware.
Cloud platforms such as AWS, Google Cloud, or Azure are commonly used by machine learning engineers for storing and processing large datasets. Familiarity with these platforms, along with services like Kubernetes for container orchestration, is a vital part of their technical toolkit. Engineers may also use version control systems like Git and GitHub to manage the various iterations of their models, ensuring that they can trace the evolution of the system and maintain accurate records of changes made during development.
Machine learning engineers must also be familiar with continuous integration and continuous delivery (CI/CD) practices. These practices enable them to deploy and monitor models efficiently, allowing for faster iteration and improvement. Given that machine learning models are rarely perfect on the first attempt, the ability to rapidly deploy changes and test different versions of a model is essential for success in this role.
The Role of Data in Machine Learning Engineering
Data is at the heart of machine learning, and machine learning engineers need to understand how to work with large and complex datasets. The models they develop are only as good as the data they are trained on. A machine learning engineer must be proficient in data preprocessing techniques such as data cleaning, transformation, and feature engineering to ensure that the input data is of high quality and suitable for training a model.
Additionally, machine learning engineers must work with data scientists to understand which features and variables are most relevant to the problem at hand. While data scientists often analyze data to extract insights and formulate hypotheses, machine learning engineers take these findings and turn them into actionable models that can be used in production environments. For example, a data scientist may discover a relationship between certain variables, and it is the machine learning engineer’s job to create an algorithm that can accurately predict future outcomes based on those variables.
The quality of the data can have a significant impact on the model’s performance. Machine learning engineers must ensure that data is properly cleaned and preprocessed before being fed into the models. This includes handling missing values, removing outliers, and ensuring that the data is formatted correctly. In many cases, engineers may also need to work with synthetic data if real-world data is insufficient or unavailable.
Challenges Faced by Machine Learning Engineers
Machine learning engineering is not without its challenges. One of the primary obstacles is ensuring that the models are both accurate and efficient. Models can be overfitted, underfitted, or fail to generalize well on new, unseen data. A key challenge for machine learning engineers is to balance model complexity with computational efficiency, ensuring that the models are scalable and can handle large volumes of data in real-time.
Another challenge is dealing with model drift. Over time, the performance of machine learning models can degrade as the underlying data changes. This phenomenon, known as model drift, can occur due to a variety of reasons, including changes in consumer behavior, environmental factors, or economic conditions. Machine learning engineers must monitor the performance of their models over time and update them as needed to ensure that they continue to provide accurate predictions.
Additionally, machine learning engineers need to stay on top of the latest developments in machine learning and artificial intelligence. The field is evolving rapidly, with new algorithms, frameworks, and techniques emerging all the time. To remain competitive, machine learning engineers must continually learn and adapt to new technologies and methodologies.
Understanding the Role of a Data Scientist
The role of a data scientist is one that involves analyzing and interpreting complex data to help organizations make data-driven decisions. While machine learning engineers focus on building and deploying models, data scientists are primarily concerned with extracting actionable insights from raw data, using statistical methods, machine learning techniques, and data analysis tools. A data scientist’s job is to convert large and complex datasets into valuable insights, identifying trends, patterns, and anomalies that can guide business strategy.
At the core of the data scientist’s responsibilities is the ability to ask the right questions. These professionals must work closely with business stakeholders to understand the problems they need to solve and the data available to them. They are experts in framing business problems as analytical tasks and then using statistical models, machine learning algorithms, and other techniques to provide meaningful answers. Data scientists must have a deep understanding of various machine learning algorithms, but their focus is often more on model evaluation, feature selection, and ensuring that the insights they generate are accurate and actionable.
A data scientist’s workflow typically begins with data collection, followed by cleaning and preprocessing. Once the data is ready, they use statistical analysis, data mining, and exploratory data analysis (EDA) techniques to identify patterns. After this, they will build predictive models, test hypotheses, and validate the results to ensure accuracy. Data scientists also play a significant role in visualizing the data and communicating insights to non-technical stakeholders, making their work accessible to people who may not have a technical background.
Data scientists are also experts in working with large datasets and often utilize big data tools such as Hadoop, Spark, and cloud-based platforms to store and process this information. They work with a wide variety of data types, including structured, semi-structured, and unstructured data, and often use advanced techniques like natural language processing (NLP), time series analysis, and deep learning.
Required Skills and Knowledge for Data Scientists
Data scientists are expected to have a well-rounded skill set that blends technical knowledge, analytical thinking, and strong communication skills. One of the primary skills needed is proficiency in programming, with languages such as Python, R, and SQL being widely used for data manipulation, analysis, and model building. Data scientists also need a strong grasp of statistical methods, as much of their work relies on hypothesis testing, probability, and inferential statistics to draw conclusions from data.
In addition to programming and statistics, data scientists must be familiar with data visualization tools like Tableau, Power BI, Seaborn, and Matplotlib, which allow them to present their findings in a way that is clear and understandable to business leaders. These visualizations are critical for communicating complex insights, making it easier for decision-makers to understand data trends and patterns that may impact the business.
Data scientists must also have a deep understanding of machine learning and data mining techniques. While they may not always deploy models into production like machine learning engineers, data scientists use these techniques to build and evaluate predictive models. They need to be familiar with both supervised and unsupervised learning methods, including decision trees, support vector machines, clustering, and regression models.
The ability to clean and preprocess data is a crucial skill for data scientists. Real-world data is often messy, incomplete, and noisy, and a significant part of a data scientist’s job involves cleaning and transforming this data into a usable format. This includes handling missing values, removing outliers, encoding categorical variables, and normalizing numerical features.
In addition to technical skills, data scientists must also be effective communicators. They need to translate their technical findings into insights that are relevant to the business and present these findings in an understandable and actionable way. Effective communication is essential, as data scientists often work in cross-functional teams with stakeholders from different backgrounds, including marketing, finance, and operations.
Data Science and Its Relationship with Business
Data scientists are integral to driving business value through data-driven decision-making. They play a key role in identifying trends and making predictions that can help businesses improve operations, optimize marketing strategies, predict customer behavior, and even reduce costs. By analyzing historical data and making projections based on that analysis, data scientists can help organizations stay competitive in a rapidly changing market.
In addition to generating insights, data scientists are also responsible for testing the assumptions that businesses make about their operations. For instance, a business may assume that increasing advertising spend leads to higher sales. A data scientist would test this hypothesis by analyzing past sales data and running experiments to determine if this assumption holds true. This ability to test and validate business assumptions is one of the most valuable aspects of a data scientist’s role.
Moreover, data scientists are crucial for enabling companies to transition into more data-driven organizations. As data becomes increasingly important in modern business practices, having data scientists on board allows companies to capitalize on the growing wealth of data they generate. By building and deploying models that can predict future events, data scientists empower organizations to act proactively rather than reactively.
The Challenges Faced by Data Scientists
Like machine learning engineers, data scientists also face significant challenges in their work. One of the most common challenges is dealing with the complexity of data. Data scientists often have to work with a wide variety of data sources, including structured and unstructured data from multiple systems. Integrating and normalizing this data can be time-consuming and difficult, particularly when the data comes from different formats, databases, or systems.
Another challenge is the need for constant experimentation. Data science is a highly iterative process, and models often need to be adjusted, retrained, and re-evaluated. This requires data scientists to be comfortable with an experimental approach, testing different models and refining them over time. Additionally, there is often a trade-off between model complexity and interpretability. Some advanced machine learning models, such as deep neural networks, can produce highly accurate predictions but are difficult to interpret. Data scientists must strike a balance between achieving high accuracy and maintaining a model that can be understood and trusted by stakeholders.
Data scientists also face the challenge of communicating complex technical results to non-technical audiences. While they may be able to generate sophisticated analyses, they need to present their findings in a way that is easy for business leaders to understand and act upon. Effective communication requires a combination of storytelling and data visualization skills, making it easier for the audience to grasp the significance of the insights.
Finally, as data science is a rapidly evolving field, staying up to date with the latest tools, techniques, and research is crucial. The fast pace of technological change means that data scientists must constantly learn and adapt, which can be both exciting and demanding.
Data scientists are at the forefront of extracting actionable insights from data, making them indispensable in today’s data-driven world. They apply their expertise in statistics, machine learning, and data visualization to help organizations solve complex problems, make informed decisions, and gain a competitive advantage. While their role differs from that of a machine learning engineer, both professionals work together to create a data-driven culture that drives innovation and success.
Data Scientist vs Machine Learning Engineer – Key Differences
In today’s technology-driven world, both data scientists and machine learning engineers play critical roles in advancing organizations’ ability to harness the power of data. While the titles may appear similar at first glance, the responsibilities, skills, and focus areas of these two roles differ significantly. In this section, we will explore the key distinctions between data scientists and machine learning engineers in terms of their roles, skill sets, and contributions to the machine learning lifecycle.
Core Responsibilities
The primary difference between a data scientist and a machine learning engineer lies in their focus and the nature of their responsibilities. Data scientists are concerned with understanding and analyzing data to extract valuable insights that guide decision-making. Their work often involves exploring data sets, identifying patterns, and developing statistical models that provide predictions or insights into a given problem. Data scientists need to be able to communicate their findings in a manner that is understandable to business leaders and stakeholders, bridging the gap between complex data and actionable business strategies.
In contrast, machine learning engineers are responsible for transforming models and algorithms into production-ready systems. They take the insights or models developed by data scientists and engineer them into scalable, efficient, and maintainable solutions. Their job is to ensure that machine learning models work effectively in real-world environments by deploying them to production, optimizing performance, and managing issues related to latency, throughput, and memory usage. While data scientists focus on building and evaluating models, machine learning engineers ensure that these models function smoothly and efficiently once they are deployed.
The Role of Data in Both Fields
The role of data is fundamental in both data science and machine learning engineering, but the way data is handled and utilized differs between the two professions. Data scientists are experts in data exploration, cleaning, and analysis. They spend a significant amount of time gathering raw data from various sources, transforming it into a structured format, and performing exploratory data analysis (EDA) to understand its structure and properties. Data scientists are also skilled at handling missing or noisy data, dealing with outliers, and performing feature engineering to ensure that the data is optimized for modeling.
On the other hand, machine learning engineers primarily focus on how to process and use data in the context of machine learning models. While they may perform some data cleaning, their primary responsibility is to ensure that data flows seamlessly through the system, is efficiently processed by machine learning models, and that the models receive the correct data inputs at scale. Machine learning engineers must have a deep understanding of how data interacts with machine learning models, ensuring that it is correctly preprocessed before training and validation.
Collaboration Between Data Scientists and Machine Learning Engineers
Despite the differences in their roles, data scientists and machine learning engineers often work closely together, particularly when it comes to building and deploying machine learning systems. Data scientists may develop an initial model or hypothesis based on their analysis of the data. Once they have identified a promising model, they pass it to machine learning engineers, who then take the model and deploy it into a production environment.
Machine learning engineers are responsible for ensuring that the model works in a scalable and efficient manner. This involves optimizing the model’s code, testing it across different platforms, and ensuring it performs well under real-world conditions. Additionally, engineers ensure that the model integrates seamlessly with the infrastructure and systems already in place within the organization, including databases, cloud platforms, and other data pipelines.
For example, data scientists may develop a machine learning model that can predict customer churn based on historical data. Once the model is validated and refined, it is passed to machine learning engineers, who will work on scaling the model to handle millions of customers and ensure that it can provide real-time predictions in production. Machine learning engineers also deal with ongoing monitoring and maintenance of the model once it is deployed, adjusting it as necessary when model performance starts to degrade or new data becomes available.
Skill Sets and Technical Expertise
The skill sets required for data scientists and machine learning engineers reflect the differing nature of their roles. Data scientists are expected to have a strong background in statistics, data analysis, and machine learning theory. They must be proficient in programming languages like Python and R, which are used extensively in data manipulation, statistical modeling, and visualization. Data scientists should also be comfortable with data analysis tools like Pandas, NumPy, and Scikit-learn for modeling and exploration.
In addition to statistical analysis, data scientists need to be skilled in data visualization. Tools such as Tableau, Power BI, Seaborn, and Matplotlib are used to create compelling visualizations that can help convey insights to business stakeholders. Data scientists must also be strong communicators, as they often need to present complex data findings to non-technical teams in a way that is accessible and actionable.
Machine learning engineers, by contrast, focus more heavily on software engineering and the practical application of machine learning models in production environments. They must have a strong understanding of data structures and algorithms, as well as expertise in programming languages like Python, Java, Scala, and C++. Machine learning engineers also need to be proficient in machine learning frameworks such as TensorFlow, PyTorch, MXNet, and Keras, which are used to build, train, and deploy models.
Beyond programming, machine learning engineers must be experts in deploying machine learning models in cloud environments like AWS, Google Cloud, and Azure. They often work with containerization tools like Docker and Kubernetes to ensure that models are deployed in a way that is scalable and efficient. Additionally, they are skilled in optimizing models for performance, ensuring that they can handle large amounts of data and make predictions in real time.
Tools and Technologies
Both data scientists and machine learning engineers use a wide range of tools and technologies, but their focus areas are different. For instance, data scientists often rely on tools such as Jupyter Notebooks, Google Colab, and SageMaker for interactive development and model building. They also work with libraries such as Scikit-learn, XGBoost, and Fast.ai for model building and training.
On the other hand, machine learning engineers typically use tools like TensorFlow, PyTorch, and ONNX for deep learning and model deployment. These tools are more suited for production-level systems that require high scalability and efficiency. Machine learning engineers also work with DevOps tools like Kubernetes, Docker, and Jenkins to automate model deployment and management. Additionally, they often utilize cloud services like AWS S3, Google Cloud Storage, and Azure Blob Storage to store and manage large datasets.
Educational Background
While both roles require strong technical skills, the educational background for each can vary. Data scientists typically come from backgrounds in mathematics, statistics, computer science, or engineering. A deep understanding of statistical analysis and machine learning algorithms is essential for this role, and many data scientists hold advanced degrees, such as a Master’s or PhD in fields related to data science or analytics.
Machine learning engineers, on the other hand, tend to have a stronger background in computer science or software engineering. They may have degrees in computer science, electrical engineering, or related fields and are often experts in software development, algorithm optimization, and system design. A solid understanding of distributed computing and cloud architecture is also crucial for machine learning engineers, as these skills are required to manage large-scale data processing and model deployment.
Career Path and Growth Opportunities
Both data scientists and machine learning engineers enjoy lucrative career prospects, with high demand for their skills across various industries. However, the growth paths for these two professions can differ.
Data scientists may evolve into roles such as senior data scientists, data science managers, or even chief data officers (CDOs) in larger organizations. Their expertise in interpreting data and creating actionable insights positions them well for leadership roles that require a combination of technical expertise and business acumen. They may also transition into specialized areas of data science, such as natural language processing (NLP), computer vision, or big data analytics.
Machine learning engineers, on the other hand, may progress to roles like senior machine learning engineer, AI architect, or machine learning engineering manager. As they gain expertise in scaling machine learning systems and deploying them into production, they may take on leadership roles in large-scale AI projects or organizations focused on developing cutting-edge AI technologies.
Choosing Between Data Scientist and Machine Learning Engineer – How Organizations Decide
With the growing prominence of data and machine learning technologies, companies are increasingly looking to build teams that can handle the complexities of data analytics, predictive modeling, and AI system development. However, deciding whether to hire a data scientist or a machine learning engineer—or both—depends on the organization’s needs, objectives, and the specific challenges it faces. In this section, we will explore how organizations can choose between these two roles, the factors influencing their decision, and the skills that are most crucial for success in each profession.
When to Hire a Data Scientist
Organizations should consider hiring a data scientist when their primary goal is to gain insights from data and use those insights to inform strategic decisions. Data scientists are crucial for businesses that rely heavily on data-driven decision-making and want to use statistical and machine learning techniques to uncover patterns, trends, and anomalies that are not immediately obvious. Some scenarios where a data scientist would be essential include:
1. Data Exploration and Analysis
If an organization has a large volume of raw or unstructured data and wants to explore and understand it better, a data scientist is the ideal candidate. Data scientists excel at extracting meaningful patterns from complex datasets and often work in industries like finance, healthcare, retail, and e-commerce, where data exploration is critical. For example, a retail company might hire a data scientist to analyze purchasing behavior and identify which factors influence customer loyalty.
2. Developing Predictive Models
Organizations that want to use historical data to make predictions about future outcomes—such as forecasting sales, predicting customer churn, or identifying fraud—should hire a data scientist. Data scientists are proficient in both supervised and unsupervised machine learning techniques, and their work typically involves creating and testing predictive models. Their ability to interpret data and fine-tune models ensures that businesses can make informed decisions based on reliable forecasts.
3. Data Visualization and Reporting
Another key aspect of a data scientist’s role is to present complex data findings in an understandable and visually appealing manner. Data scientists are skilled in using data visualization tools like Tableau, Power BI, and Python libraries (Matplotlib, Seaborn) to create dashboards, charts, and reports that help non-technical stakeholders interpret results. For organizations that need to communicate insights effectively to senior management or other departments, data scientists provide a crucial bridge between technical data analysis and business strategy.
4. Problem Solving and Hypothesis Testing
Data scientists often work with business teams to define key metrics and evaluate hypotheses. For example, an e-commerce business may hypothesize that offering discounts on specific products increases sales, and a data scientist would be tasked with analyzing sales data to validate or reject that hypothesis. Data scientists use statistical tests, A/B testing, and experimentation to guide decision-making based on data.
When to Hire a Machine Learning Engineer
On the other hand, organizations should look to hire a machine learning engineer when they need to take machine learning models from concept to production. While data scientists develop and test models, machine learning engineers are responsible for ensuring that these models can be used effectively at scale, integrated with other software systems, and optimized for real-world applications. Here are some common situations where hiring a machine learning engineer is essential:
1. Deploying Models into Production
Machine learning engineers are key when the organization needs to deploy machine learning models into production environments. This involves not just developing models, but making them robust, scalable, and capable of handling large volumes of data. For example, if an e-commerce company has a recommendation engine that needs to make personalized product suggestions in real-time, machine learning engineers will be responsible for ensuring that the recommendation system operates smoothly at scale.
2. Optimizing and Scaling Models
Once models have been developed, they often need to be optimized for performance, speed, and memory usage. Machine learning engineers specialize in optimizing algorithms for faster training and prediction times, as well as ensuring that models work efficiently with large datasets. For instance, in high-performance applications such as autonomous vehicles or video streaming services, machine learning engineers play a critical role in fine-tuning models to meet strict performance requirements.
3. Real-Time and Big Data Applications
Organizations that need to handle real-time data streams, such as fraud detection systems or dynamic pricing models, will require machine learning engineers to build infrastructure that supports low-latency, high-throughput systems. For instance, financial institutions using real-time market data to make trading decisions will need engineers to ensure that models can process and make predictions instantly, without delays that could result in missed opportunities.
4. Integration with Existing Systems
Machine learning engineers are responsible for integrating machine learning models with the rest of the company’s software infrastructure. This includes working with databases, APIs, and cloud platforms to ensure that the models can be used seamlessly within the organization’s ecosystem. For example, if a company develops a machine learning-based recommendation system, the engineer will need to work with backend engineers to integrate it into the company’s website or app.
5. Model Maintenance and Updates
Machine learning engineers also handle the ongoing maintenance of deployed models. As new data becomes available or as the model’s performance starts to degrade over time (a phenomenon known as model drift), machine learning engineers are responsible for retraining and fine-tuning the models to ensure continued accuracy. They are also involved in version control, ensuring that the right versions of models are used and that performance can be monitored continuously.
Skills Crucial for Success in Data Science
Data scientists require a combination of technical skills, analytical thinking, and strong communication abilities to succeed. Below are some of the most important skills that data scientists need:
1. Statistical Analysis and Mathematical Expertise
Data scientists must have a strong grasp of statistics, including hypothesis testing, probability, and regression analysis. Understanding the theory behind statistical methods allows them to design experiments, analyze data rigorously, and interpret results accurately.
2. Data Manipulation and Cleaning
The ability to preprocess and clean data is essential for data scientists. They must be proficient in handling missing values, dealing with outliers, and transforming raw data into a format suitable for analysis.
3. Programming and Scripting
Proficiency in programming languages such as Python, R, and SQL is crucial. These languages are commonly used for data manipulation, statistical modeling, and data visualization.
4. Machine Learning Knowledge
Although data scientists are not responsible for deploying models, they must have a good understanding of machine learning algorithms, both supervised and unsupervised, and how to evaluate model performance.
5. Data Visualization
Data scientists need to be skilled in presenting their findings visually through graphs, dashboards, and charts. Tools such as Tableau, Power BI, and Python visualization libraries (Seaborn, Matplotlib) are commonly used.
Skills Crucial for Success in Machine Learning Engineering
Machine learning engineers need a different set of skills, focused on the practical application of machine learning models and their deployment at scale. The key skills for success in this role include:
1. Software Engineering
Machine learning engineers must have strong software engineering skills, including proficiency in programming languages like Python, Java, Scala, and C++. They also need a deep understanding of algorithms and data structures to optimize models for performance.
2. Cloud and Big Data Platforms
Machine learning engineers should be familiar with cloud platforms like AWS, Google Cloud, and Azure, as well as big data tools like Hadoop and Spark. These tools help them scale machine learning applications and manage large datasets.
3. Machine Learning Frameworks
In-depth knowledge of machine learning frameworks such as TensorFlow, PyTorch, Keras, and Scikit-learn is essential for building and deploying machine learning models.
4. Model Deployment and Optimization
Machine learning engineers specialize in deploying models into production, optimizing them for performance, and ensuring they work efficiently in real-world environments. Knowledge of containerization (e.g., Docker), orchestration (e.g., Kubernetes), and CI/CD tools is often required.
5. System Design and Integration
Machine learning engineers need to be able to integrate models into existing systems. They must work with APIs, databases, and microservices to ensure that the models can seamlessly interact with other parts of the organization’s technology stack.
Conclusion
Deciding between hiring a data scientist or a machine learning engineer depends on the organization’s objectives and the specific challenges they face. If the organization’s primary goal is to derive insights from data, make predictions, and drive strategic decisions, a data scientist will be the best fit. However, if the organization needs to deploy machine learning models at scale, optimize their performance, and integrate them into existing systems, a machine learning engineer is the better choice.
In many cases, organizations will need both roles. Data scientists and machine learning engineers often work together to create end-to-end data solutions, with data scientists focusing on model development and machine learning engineers handling model deployment and optimization. Both roles are integral to an organization’s ability to leverage machine learning and AI to drive innovation and stay competitive in the data-driven world.
As both fields continue to evolve, organizations should stay attuned to the unique skill sets and responsibilities of each role to ensure they build the right team to meet their goals.